Тест за отравяне на данни и задни атаки

Отговорите и обясненията се появяват, докато отговаряте на теста.

1. Which goal distinguishes a triggered backdoor from general availability poisoning?
   • Attacker-chosen behavior associated with a specified trigger
   • A requirement that every clean input must be misclassified
   • A guarantee that all triggered inputs succeed without errors
   • A requirement that only image models can be attacked
2. What does clean-label poisoning preserve in its modified training examples?
   • A guarantee that every example is harmless
   • Every original input byte with no change
   • A label appropriate to the training item
   • A guarantee that no defense can detect the poison
3. Какво показа известното изследване на BadNets?
   • По-бърз алгоритъм за обучение
   • Знак „Стоп“, погрешно класифициран като знак за ограничение на скоростта чрез задействане на стикер
   • Нов оптимизатор
   • Начин за компресиране на модели
4. Как изследователите показаха, че масивите от данни в уеб мащаб могат да бъдат отровени евтино?
   • Чрез хакване на облачни сървъри
   • Чрез подкупване на анотатори
   • Чрез претоварване на мрежата
   • Чрез закупуване на домейни с изтекъл срок на валидност, хостващи част от URL адресите на изображения на набор от данни
5. What does Spectral Signatures in Backdoor Attacks study as a defense signal?
   • A proof that all larger batches eliminate triggers
   • Only document filenames sorted alphabetically
   • Suspicious statistical structure in learned representations
   • A guarantee that all clean accuracy equals zero
6. Why can a dataset index pointing to remote content have a split-view poisoning exposure?
   • The indexed resource can change before a later downloader retrieves it
   • Every URL necessarily points to an immutable archived copy
   • All dataset labels are automatically rewritten at inference
   • A domain’s first owner must keep permanent control forever
7. Which stage is directly manipulated in a data-poisoning attack, as opposed to an inference-only input perturbation?
   • Only the final user-interface font
   • Only a previously fixed output screenshot
   • Only the displayed model name
   • The training data or learning process
8. What reference makes a content hash useful against the cited split-view poisoning threat?
   • A fresh hash supplied solely by the attacker alongside the changed resource
   • A trusted hash of the raw resource obtained before the malicious change
   • Only an unchanged URL string
   • A requirement that the resource keep the same file extension